The cytometry and biostatistics core is a central component of the overall program project and provides quantitative cell analysis, database, and biostatistics support. Working in conjunction with the other cores and projects, it identifies promising markers and uses them to create and validate stochastic models that describe and predict more accurately than do current models the underlying pathobiology and clinical behavior of human breast cancer. These stochastic models build on conventional factors, such as clinical staging, histopathologic grading, age, and estrogen receptor status, but also include tumor markers having additional diagnostic and prognostic power. Our goal is to be able to stratify patients into different risk and therapeutic groups based on the fundamental pathobiology of their lesions. This core is responsible for: I. Cytokinetic and Cytometric Analysis of specimens obtained from the Clinical and Cell Biology Cores, including surgical "bench" specimens of primary cancers and of nonmalignant tissues, fine needle aspirates of primary and secondary breast cancers, and cell cultures derived from malignant and non-malignant tissues. Specific analytical procedures include: 1. Cell proliferative activity by in vivo or in vitro BrdUrd labelling. 2. Cell DNA content by flow and image cytometry. 3. Heterogeneity of cell proliferation, as shown by the pattern of in vivo BrdUrd labelling. 4. Characterization, by cytokinetics, DNA cytometry, and markers, of benign host epithelial and inflammatory cells present in or adjacent to malignant tissue and of similar cells from more distant sites. 5. DNA tissue morphometrics by image cytometry. II. Database Design, Implementation and Maintenance for all relevant specimen data, including clinical and pathologic information, patient description and follow-up, fate of samples, cytometric data, and results generated by the other cores and projects. III. Biostatistical and Epidemiologic Analysis, including tabulation, description, and presentation of data, and assessment of the diagnostic and prognostic potential of our approaches. Statistical models will be constructed and tested for their predictive value. End-points are clinical behavior of tumors in terms of patient parameters such as time to recurrence, survival without evidence of disease, or satisfactory versus unsatisfactory response to therapy.